Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Short-term wind power forecasting method based on long-term and short-term memory network

A long-short-term memory, short-term forecasting technology, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as difficulty in dispatching wind power in power systems, wind curtailment, etc., to achieve the effect of excellent forecasting and generalization ability and good forecasting effect

Active Publication Date: 2019-01-15
NORTH CHINA UNIV OF WATER RESOURCES & ELECTRIC POWER
View PDF4 Cites 56 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] Aiming at the problems of wind power scheduling difficulty and wind abandonment in the power system caused by the randomness and ambiguity of wind power, the present invention provides a short-term prediction method of wind power based on long-term short-term memory network to improve the accuracy of wind power prediction

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Short-term wind power forecasting method based on long-term and short-term memory network
  • Short-term wind power forecasting method based on long-term and short-term memory network
  • Short-term wind power forecasting method based on long-term and short-term memory network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0113] Wind power plays a dominant role in the development of renewable energy, but the randomness and ambiguity of wind power make it difficult for the power system to dispatch wind power, and the phenomenon of wind abandonment is serious. Improving the accuracy of wind power forecasting is an effective measure to solve this problem. The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0114] 1 Long short-term memory neural network

[0115] 1.1 Principle of LSTM neural network

[0116] Recurrent Neural Networks (RNNs) constitute a very powerful computational model capable of instantiating nearly arbitrary dynamics. However, the extent to which this potential can be exploited is limited by the effectiveness of the training procedures employed. Gradient-based methods—learning through time or real-time recurrent learning and their combined backpropagation—share an important limitation. On all "time flow back" ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a wind power short-term prediction method based on a long-term and short-term memory network, comprising a long-term and short-term memory neural network training algorithm, ashort-term wind power prediction error distribution algorithm and a wind turbine generator power short-term prediction model design. A long-term and short-term memory network algorithm (LSTM)-based wind pow prediction model is established based on the depth learning network, and the Gaussian mixture model (GMM) is used to analyze the error distribution characteristics of the short-term wind powerprediction. The invention can obtain different confidence intervals of two units through the GMM model. It is proved that LSTM method has higher precision and faster convergence rate, and GMM method has practical application value for wind power dispatching.

Description

Technical field: [0001] The invention belongs to the technical field of wind power prediction technology, in particular to a short-term wind power prediction method based on a long-short-term memory network. [0002] The method is as follows: Step 1: Establish an LSTM network model for wind power prediction; Step 2: Establish a GMM model for the error distribution of wind power prediction values; Step 3: Evaluation of the performance and effect of the LSTM prediction model and the GMM model. [0003] The short-term wind power prediction method established by the LSTM in the present invention has a better prediction effect for large sample data, and its prediction and generalization capabilities are better. Background technique: [0004] Due to the abundance of wind energy resources and its cleanness and environmental protection, wind power plays a leading role in the form of renewable energy power generation. As of the end of 2017, my country's newly installed wind power ca...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 张晋华程鹏黄慧李茂茗
Owner NORTH CHINA UNIV OF WATER RESOURCES & ELECTRIC POWER
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com